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Is there a way I could find the Latin root/definition of said root in a word with NLTK (and possibly wordnet)?
Thanks so much!
What you need are lemmatizers. A Latin lemmatizer is:
LemLat [for one word, whole text document]
A Greek lemmatizer:
The Grammatical Tagger by ELTL [download]
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I need to write a program to decode a permutation cipher. Could you help me? Thanks.
The input of the procedure should include the initial phrase and the number of periods. There is an encryption key and it has to be converted to reverse. Use strings please.
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I need to identify the songs by human hum. what are the best methodology and algorithm that i can use for achieve that. I search for code samples. But I couldn't find. Please help me....
You could begin a python program that uses tensorflow to deep-learn the correspondence between humming and songs - it should fall under the umbrella initiative by Google Brain called Magenta.
Of course for Deep-Learning you would need to have a large corpus of examples to learn from.
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I want to check below sentence is either positive or negative please help me
Sentence -I don't remember anything
Is this what you are looking for? (In Python)
from textblob import TextBlob
TextBlob("not a very great calculation").sentiment
## Sentiment(polarity=-0.3076923076923077, subjectivity=0.5769230769230769)
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I have a phrase like this one:
Apple iPhone 5 White 16GB
and I want to tag in this way
B M C S
where
B=Brand (Apple)
M=Model (iPhone 5)
C=Color (White)
S=Size (Size)
A classifier must learn the sequence pattern... I think that I will use SVM or CRF.
My question is what is the best way to tag a phrase like this? I will use the NLTK library for python.
What you think of {Apple}\B {iPhone 5}\M....? What is the best way?
Is there also a way to use a seed dictionary (of brands for example) to let NLTK automatic tagging a list of phrases for me?
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I am trying to find top 100/1000 words based on tfidfVectorizer output of Python's scikit-learn library. Is there a way to do it using a function from the scikit libraries?
Thanks for help
What do you mean by top 100/1000 words? The most frequent words in a dataset? You can use the Counter class of the Python standard library to do that. No need for scikit-learn.